The Human3.6M dataset has been widely used to evaluate 3D single person pose estimation. As our task is focused on camera-centric 3D-MPE to handle inter-person occlusion and depth ambiguity, we do not expect our method to perform significantly better than the SOTA methods. The MRPE of root joint localization results on Human3.6M dataset are shown in Table REF . The top 3 rows are baseline results that take from the figures reported in [1]}. The baseline methods [2]}, [3]} employed an optimization process to minimize the error of back-projection between 2D and 3D pose. “w/o limb joints” refers to optimization using only head and body trunk joints. “with RANSAC” refers to randomly sampling the set of joints used for optimization with RANSAC.
As shown in Table 1, our method is slightly superior to the SOTA top-down method [1]}. The main reason is that Human3.6M is an indoor single-person dataset, which is quite clean with almost no crowded scenarios. Notably, [5]} and [1]} are used Mask-Rcnn [7]} with the help of large detection datasets, which resulting our method got slightly lower accuracy along x- and y-axis. Despite this, our method shows better depth location ability at camera-centric coordinates.
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